• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• Journal of Korean Dental Science
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• v.12 no.1
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• pp.13-19
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• 2019

Purpose: The aim of this study was to compare the flexural strength of provisional fixed dental prostheses which was three-dimensional (3D) printed by several build directions. Materials and Methods: A metal jig with two abutment teeth and pontic space in the middle was fabricated. This jig was scanned with a desktop scanner and provisional restoration was designed on dental computer-aided design program. On the preprocessing software, the build angles of the restorations were arranged at $0^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, and $90^{\circ}$ and support was added and resultant structure was sliced to a thickness of $100{\mu}m$. Processed restorations were printed with digital light processing type 3D printer using poly methyl meta acrylate-based resin. After washing and post-curing, compressive loading was applied at a speed of 1 mm/min on a metal jig fixed to a universal testing machine. The maximum pressure at which fracture occurred was measured. For the statistical analysis, build direction was set as the independent variable and fracture strength as the dependent variable. One-way analysis of variance and Tukey's post hoc analysis was conducted to compare fracture strength among groups (${\alpha}=0.05$). Result: The mean flexural strength of provisional restoration 3D printed with the build direction of $0^{\circ}$ was $1,053{\pm}168N$; it was $1,183{\pm}188N$ at $30^{\circ}$, $1,178{\pm}81N$ at $45^{\circ}$, $1,166{\pm}133N$ at $60^{\circ}$, and $949{\pm}170N$ at $90^{\circ}$. The group with a build direction of $90^{\circ}$ showed significantly lower flexural strength than other groups (P<0.05). The flexural strength was significantly higher when the build direction was $30^{\circ}$ than when it was $90^{\circ}$ (P<0.01). Conclusion: Among the build directions $0^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, and $90^{\circ}$ set for 3D printing of fixed dental prosthesis, an orientation of $30^{\circ}$ is recommended as an effective build direction for 3D printing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.755-758
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• 2014

In this paper, we propose effective Deblocking Filter hardware architecture for High Efficiency Video Coding encoder. we propose Deblocking Filter hardware architecture with less processing time, filter ordering for low area design, effective memory architecture and four-pipeline for a high performance HEVC(High Efficiency Video Coding) encoder. Proposed filter ordering can be used to reduce delay according to preprocessing. It can be used for realtime single-port SRAM read and write. it can be used in parallel processing by using two filters. Using 10 memory is effective for solving the hazard caused by a single-port SRAM. Also the proposed filter can be used in low-voltage design by using clock gating architecture in 4-pipeline. The proposed Deblocking Filter encoder architecture is designed by Verilog HDL, and implemented by 100k logic gates in TSMC $0.18{\mu}m$ process. At 150MHz, the proposed Deblocking Filter encoder can support 4K Ultra HD video encoding at 30fps, and can be operated at a maximum speed of 200MHz.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Geophysics and Geophysical Exploration
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• v.22 no.1
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• pp.21-28
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• 2019

We perform a 3D tomographic inversion using surface wave dispersion curves to obtain S-velocity model and radial anisotropy beneath Saudi Arabia. The Arabian Peninsula is geologically and topographically divided into a shield and a platform. We used event data with magnitudes larger than 5.5 and epicentral distances shorter than $40^{\circ}$ during 2008 ~ 2014 from the Saudi Geological Survey. We obtained dispersion curves by using the multiple filtering technique after preprocessing the event data. We constructed SH- and SV-velocity models and consequently radial anisotropy model at 10 ~ 60 km depths by inverting Love and Rayleigh group velocity dispersion curves with period ranges of 5 ~ 140 s, respectively. We observe high-velocity anomalies beneath the Arabian shield at 10 ~ 30 km depths and low-velocity anomalies beneath the Arabian platform at 10 km depth in the SV-velocity model. This discrepancy may be caused by the difference between the Arabian shield and the Arabian platform, that is, the Arabian shield was formed in Proterozoic thereby old and cold, while the Arabian platform is covered by predominant Paleozoic, Mesozoic, and Cenozoic sedimentary layers. Also we obtained radial anisotropy by estimating the differences between SH- and SV-velocity models. Positive anisotropy is observed, which may be generated by lateral tension due to the slab pull of subducting slabs along the Zagros belt.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.99-109
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• 2021

In recent machine learning studies, in order to consider the quality and completeness of data, derivation of non-functional requirements for data has been proposed from the viewpoint of requirements engineering. In particular, requirements engineers have defined data requirements in machine learning. In this study, data requirements were derived at the data acquisition (DAQ) stage, where data is collected and stored before data preprocessing. Through this, it is possible to express the requirements of all data required in the existing DAQ system, the presence of tasks (functions) satisfying them, and the relationship between the requirements and functions. In addition, it is possible to elicit requirements and to define the relationship, so that a software design document can be produced, and a systematic approach and direction can be established in terms of software design and maintenance. This research using existing DAQ system cases, scenarios and use cases for requirements engineering approach are created, and data requirements for each case are extracted based on them, and the relationship between requirements, functions, and goals is illustrated through goal modeling. Through the research results, it was possible to extract the non-functional requirements of the system, especially the data requirements, from the DAQ system using requirements engineering.